Title: Microbe-Metabolite Interactions-Based Metabolic Profiles Predictor
Version: 0.1.0
Description: Implements a computational framework to predict microbial community-based metabolic profiles with 'O2PLS' model. It provides procedures of model training and prediction. Paired microbiome and metabolome data are needed for modeling, and the trained model can be applied to predict metabolites of analogous environments using new microbial feature abundances.
Depends: R (≥ 4.1.0)
Imports: magrittr (≥ 2.0.1), OmicsPLS (≥ 2.0.2), utils, stats
Suggests: rmarkdown, knitr, prettydoc, testthat (≥ 3.0.0)
License: GPL (≥ 3.0)
URL: https://github.com/YuLab-SMU/MMINP
BugReports: https://github.com/YuLab-SMU/MMINP/issues
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.0
Config/testthat/edition: 3
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2022-07-04 03:45:54 UTC; Dell
Author: Wenli Tang ORCID iD [aut, cre], Guangchuang Yu ORCID iD [aut, ths]
Maintainer: Wenli Tang <1071429394@qq.com>
Repository: CRAN
Date/Publication: 2022-07-04 18:50:05 UTC

Predict metabolites from new microbiome samples using MMINP model.

Description

This function aims to predict potentially metabolites in new microbial community using trained MMINP model. If genes in model are not appear in newdata, then this procedure will fill them up with 0. Note that this function does not center or scale the new microbiome matrixs, you would better do preprocessing on newdata in advance.

Usage

MMINP.predict(model, newdata, minGeneSize = 0.5)

Arguments

model

List of class "mminp" or "o2m", produced by MMINP.train or o2m.

newdata

New matrix of microbial genes, each column represents a gene.

minGeneSize

A numeric between 0-1, minimal size of genes in model contained in newdata.

Details

The model must be class 'mminp' or 'o2m'. The column of newdata must be microbial genes.

Value

Predicted Data

Examples

data(MMINP_trained_model)
data(test_metag)
test_metag_preprocessed <- MMINP.preprocess(test_metag, normalized = FALSE)
pred_metab <- MMINP.predict(model = MMINP_trained_model$model,
newdata = test_metag_preprocessed)

Data Preprocessing function for MMINP

Description

Before doing MMINP analysis, abundances of both microbial features and metabolites should be preprocessed. Both measurements are expected to be transformed to relative abundance (i.e. proportion) and be log-transformed. To meet the need of O2-PLS method, data must be scaled.

Usage

MMINP.preprocess(
  data,
  normalized = TRUE,
  prev = NA,
  abund = NA,
  logtransformed = TRUE,
  scaled = TRUE
)

Arguments

data

A numeric matrix or data frame containing measurements of metabolites or microbial features.

normalized

Logical, whether to transform measurements into relative abundance or not.

prev

A numeric ranging from 0 to 1, the minimum prevalence of features to be retained. If set to NA, means no need to filter prevalence.

abund

A numeric greater than 0, the minimum abundance (mean) of features to be retained. If set to NA, means no need to filter abundance.

logtransformed

Logical, whether do log transformation or not.

scaled

Logical, whether scale the columns of data or not.

Details

The rows of data must be samples and columns of data must be metabolites or microbial features. The filtering process (prev and abund) is before log transformation and scale transformation.

Value

A preprocessed numeric matrix for analysis of MMINP.

Examples

data(train_metag)
d <- MMINP.preprocess(train_metag)
d <- MMINP.preprocess(train_metag, prev = 0.3, abund = 0.001)
d[1:5, 1:5]

Train MMINP model using paired microbial features and metabolites data

Description

This function contains three steps. Step1, Build an O2-PLS model and use it to predict metabolites profile; Step2, Compare predicted and measured metabolites abundances, then filter those metabolites which predicted poorly (i.e. metabolites of which correlation coefficient less than rsignif or adjusted pvalue greater than psignif.); Step3, (iteration) Re-build O2-PLS model until all reserved metabolites are well-fitted.

Usage

MMINP.train(
  metag,
  metab,
  n = 1:3,
  nx = 0:3,
  ny = 0:3,
  seed = 1234,
  compmethod = NULL,
  nr_folds = 3,
  nr_cores = 1,
  rsignif = 0.4,
  psignif = 0.05,
  recomponent = FALSE
)

Arguments

metag

Training data of sequence features' relative abundances. Must have the exact same rows (subjects/samples) as metab.

metab

Training data of metabolite relative abundances. Must have the exact same rows (subjects/samples) as metag.

n

Integer. Number of joint PLS components. Must be positive. More details in crossval_o2m and crossval_o2m_adjR2.

nx

Integer. Number of orthogonal components in metag. Negative values are interpreted as 0. More details in crossval_o2m and crossval_o2m_adjR2.

ny

Integer. Number of orthogonal components in metab. Negative values are interpreted as 0. More details in crossval_o2m and crossval_o2m_adjR2.

seed

a random seed to make the analysis reproducible, default is 1234.

compmethod

A character string indicating which Cross-validate procedure of O2PLS is to be used for estimating components, must be one of "NULL", "cvo2m" or "cvo2m.adj". If set to "NULL", depends on the features number.

nr_folds

Positive integer. Number of folds to consider. Note: kcv=N gives leave-one-out CV. Note that CV with less than two folds does not make sense. More details in crossval_o2m and crossval_o2m_adjR2.

nr_cores

Positive integer. Number of cores to use for CV. You might want to use detectCores(). Defaults to 1. More details in crossval_o2m and crossval_o2m_adjR2.

rsignif

A numeric ranging from 0 to 1, the minimum correlation coefficient of features which considered as well-predicted features.

psignif

A numeric ranging from 0 to 1, the maximum adjusted p value of features which considered as well-predicted features.

recomponent

Logical, whether re-estimate components or not during each iteration.

Value

A list containing

model

O2PLS model

trainres

Final correlation results between predicted and measured metabolites of training samples

components

Components number. If recomponent = TRUE, the components number is the result of last estimation.

re_estimate

Re-estimate information, i.e. whether re-estimate components or not during each iteration

trainnumb

Iteration number

Examples

data(test_metab)
data(test_metag)
a <- MMINP.preprocess(test_metag[, 1:20], normalized = FALSE)
b <- MMINP.preprocess(test_metab[, 1:20], normalized = FALSE)
mminp_model <- MMINP.train(metag = a,
                           metab = b,
                           n = 3:5, nx = 0:3, ny = 0:3,
                           nr_folds = 2, nr_cores = 1)
length(mminp_model$trainres$wellPredicted)

(Data) A MMINP model

Description

This model was built using (MMINP.train) with preprocessed values in dataset train_metag and train_metab.

Format

A list containing an 'o2m' model, results of correlation analysis between metabolites of training data and its predicted values, components number, re-estimate information and iteration number of modeling.

Examples

data(MMINP_trained_model)

Check if input data satisfies input conditions

Description

This function throws an error if x is not a numeric matrix or a data frame with all numeric-alike variables, or if any elements of x is NA.

Usage

checkInputdata(x)

Arguments

x

A matrix or data frame.

Value

No return value

Compare features' abundance obtained by prediction and measurement.

Description

Compare features' abundance obtained by prediction and measurement.

Usage

compareFeatures(
  predicted,
  measured,
  method = "spearman",
  adjmethod = "fdr",
  rsignif = 0.3,
  psignif = 0.05
)

Arguments

predicted

A matrix or data frame. The feature table obtained by prediction.

measured

A matrix or data frame. The feature table obtained by measurement. The abundances are expected to be normalized (i.e. proportion) or be preprocessed by MMINP.preprocess.

method

A character string indicating which correlation coefficient is to be used for the cor.test. One of "pearson", "kendall", or "spearman", can be abbreviated.

adjmethod

A character string indicating correction method (p.adjust). One of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none", can be abbreviated.

rsignif

A numeric ranging from 0 to 1, the minimum correlation coefficient of features which considered as well-predicted features.

psignif

A numeric ranging from 0 to 1, the maximum adjusted p value of features which considered as well-predicted features.

Value

A list containing a table of correlation results and a vector of well-predicted features.

Filter features of input table according to prevalence and/or abundance

Description

Filter features of input table according to prevalence and/or abundance.

Usage

filterFeatures(x, prev = NA, abund = NA)

Arguments

x

A matrix or data frame.

prev

A numeric ranging from 0 to 1, the minimum prevalence of features to be retained. If set to NA, means no need to filter prevalence.

abund

A numeric greater than 0, the minimum abundance (mean) of features to be retained. If set to NA, means no need to filter abundance.

Value

A filtered feature table will be returned.

Examples

data(train_metag)
d <- filterFeatures(train_metag, prev = 0.8)
dim(train_metag)
dim(d)

Estimate components for O2-PLS method

Description

get components number using Cross-validate procedure of O2-PLS

Usage

get_Components(
  metag,
  metab,
  compmethod = NULL,
  n = 1:10,
  nx = 0:5,
  ny = 0:5,
  seed = 1234,
  nr_folds = 3,
  nr_cores = 1
)

Arguments

metag

Training data of sequence features' relative abundances. Must have the exact same rows (subjects/samples) as metab.

metab

Training data of metabolite relative abundances. Must have the exact same rows (subjects/samples) as metag.

compmethod

A character string indicating which Cross-validate procedure of O2PLS is to be used for estimating components, must be one of "NULL", "cvo2m" or "cvo2m.adj". If set to "NULL", depends on the features number.

n

Integer. Number of joint PLS components. Must be positive. More details in crossval_o2m and crossval_o2m_adjR2.

nx

Integer. Number of orthogonal components in metag. Negative values are interpreted as 0. More details in crossval_o2m and crossval_o2m_adjR2.

ny

Integer. Number of orthogonal components in metab. Negative values are interpreted as 0. More details in crossval_o2m and crossval_o2m_adjR2.

seed

a random seed to make the analysis reproducible, default is 1234.

nr_folds

Positive integer. Number of folds to consider. Note: kcv=N gives leave-one-out CV. Note that CV with less than two folds does not make sense. More details in crossval_o2m and crossval_o2m_adjR2.

nr_cores

Positive integer. Number of cores to use for CV. You might want to use detectCores(). Defaults to 1. More details in crossval_o2m and crossval_o2m_adjR2.

Value

A data frame of components number

get components number from Cross-validate procedure of O2PLS

Description

get components number from Cross-validate procedure of O2PLS

Usage

get_cvo2mComponent(x)

Arguments

x

List of class "cvo2m", produced by crossval_o2m.

Value

A data frame of components number

Print function for MMINP.train

Description

This function is the print method for MMINP.train.

Usage

## S3 method for class 'mminp'
print(x, ...)

Arguments

x

A model (an object of class "mminp")

...

additional parameters

Value

Brief information about the object.

(Data) Normalized metabolite abundances for MMINP prediction

Description

This datasets were built from NLIBD dataset (Franzosa et al., 2019) by converting original HMDB IDs into KEGG compound IDs and removing unassigned and repeated features.

Format

A data frame of metabolite relative abundances (i.e. proportion), with 65 subjects in rows and 130 KEGG compound IDs in columns.

References

Franzosa EA et al. (2019). Gut microbiome structure and metabolic activity in inflammatory bowel disease. Nature Microbiology 4(2):293-305.

Examples

data(test_metab)

(Data) Normalized gene family abundances for MMINP prediction

Description

This datasets were built from NLIBD dataset (Franzosa et al., 2019) by converting original UniRef90 IDs into KEGG Orthology (KO) IDs and removing unassigned and repeated features.

Format

A data frame of gene family relative abundances (i.e. proportion), with 65 subjects in rows and 629 KEGG Orthology (KO) IDs in columns.

References

Franzosa EA et al. (2019). Gut microbiome structure and metabolic activity in inflammatory bowel disease. Nature Microbiology 4(2):293-305.

Examples

data(test_metag)

(Data) Normalized metabolite abundances for MMINP training

Description

This datasets were built from PRISM dataset (Franzosa et al., 2019) by converting original HMDB IDs into KEGG compound IDs and removing unassigned and repeated features.

Format

A data frame of metabolite relative abundances (i.e. proportion), with 155 subjects in rows and 135 KEGG compound IDs in columns.

References

Franzosa EA et al. (2019). Gut microbiome structure and metabolic activity in inflammatory bowel disease. Nature Microbiology 4(2):293-305.

Examples

data(train_metab)

(Data) Normalized gene family abundances for MMINP training

Description

This datasets were built from PRISM dataset (Franzosa et al., 2019) by converting original UniRef90 IDs into KEGG Orthology (KO) IDs and removing unassigned and repeated features.

Format

A data frame of gene family relative abundances (i.e. proportion), with 155 subjects in rows and 733 KEGG Orthology (KO) IDs in columns.

References

Franzosa EA et al. (2019). Gut microbiome structure and metabolic activity in inflammatory bowel disease. Nature Microbiology 4(2):293-305.

Examples

data(train_metag)